ChatGPT Prompt Engineering for Developers | Notes and Sample Codes

35 min readApr 7, 2024

Prompt engineering refers to the process of refining and crafting effective prompts for language models like ChatGPT. It involves carefully designing instructions or queries to elicit the desired response from the model.

In this article, I’ll share my notes from the course, take you to delve into the key points covered in the class, and highlight the importance of prompt engineering using large language models (LLMs) as a developer tool. (Specifically for Instruction-tuned LLMs)

For comprehensive information, kindly access the following link to gain insights into ChatGPT Prompt Engineering for Developers. It’s 100% free.

Introduction
Guidelines
Iterative
Summarizing
Inferring
Transforming
Expanding
Chatbot

Prompt engineering is crucial because language models like GPT-3.5, while powerful, require specific instructions to generate accurate and relevant outputs. By providing well-structured and explicit prompts, developers can guide the model’s responses and ensure they align with their intentions.

Effective prompt engineering involves considering various factors such as:

Context: Providing relevant context to the model can help it better understand the desired task or question. This can include background information, specific examples, or explicit instructions on what the model should focus on.
Formatting: Structuring the prompt clearly and concisely can help the model understand the expected output format. For example, using placeholders or specifying the type of response required (e.g., a list, a summary, or a detailed explanation).
Instructions: Clearly defining the task or problem the model needs to solve can help guide its responses. This may involve specifying the desired output, providing step-by-step instructions, or asking the model to think through a particular scenario.
Prompts for clarification: In certain cases, it can be beneficial to include prompts that explicitly ask the model to ask clarifying questions if it encounters ambiguity in the instructions. This can help improve the accuracy of the generated response.
Iterative refinement: Prompt engineering often involves an iterative process of experimentation and refinement. Developers may need to test different prompts, assess the model’s performance, and make adjustments based on the results.

Let’s go through the principles and tactics you outlined for working with language models like ChatGPT.

Principle 1: Write clear and specific instructions
Clear and specific instructions are essential for guiding the model’s responses accurately. By providing explicit guidance, you can help ensure that the model understands the desired task or question. This principle involves carefully crafting prompts that leave little room for ambiguity. It may include providing context, specifying the expected output format, or giving step-by-step instructions.

Principle 2: Give the model time to think
Language models like ChatGPT may require some time to generate responses, especially for complex or lengthy prompts. Allowing the model sufficient thinking time can improve the quality of its output. It’s important to be patient and wait for the model to provide a complete response before evaluating its effectiveness.

Before diving into the specific tactics with examples, we have the necessary setup in place as follow.

Setup

Load the API key and relevant Python libraries.

If you haven’t installed this Python library, you can install it using pip like this:

!pip install openai

The library needs to be configured with your account’s secret key, which is available on the website. You can either set it as the OPENAI_API_KEY environment variable before using the library:

!export OPENAI_API_KEY='sk-...'

Or, set openai.api_key to its value:

import openai
openai.api_key = "sk-..."

Helper function

Throughout this course, we will use OpenAI’s gpt-3.5-turbo model and the chat completions endpoint. This helper function will make it easier to use prompts and look at the generated outputs:

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

Prompting Principals

Principle 1: Write clear and specific instructions

Tactic 1: Use delimiters to clearly indicate distinct parts of the input

Delimiters can be anything like: ```, “””, < >, <tag> </tag>, :

For example:

text = f"""
You should express what you want a model to do by \ 
providing instructions that are as clear and \ 
specific as you can possibly make them. \ 
This will guide the model towards the desired output, \ 
and reduce the chances of receiving irrelevant \ 
or incorrect responses. Don't confuse writing a \ 
clear prompt with writing a short prompt. \ 
In many cases, longer prompts provide more clarity \ 
and context for the model, which can lead to \ 
more detailed and relevant outputs.
"""
prompt = f"""
Summarize the text delimited by triple backticks \ 
into a single sentence.
```{text}```
"""
response = get_completion(prompt)
print(response)

Clear and specific instructions should be provided to guide a model 
towards the desired output, and longer prompts can provide more clarity 
and context for the model, leading to more detailed and relevant outputs.

Tactic 2: Ask for a structured output

To make parsing the model outputs easier, it can be helpful to ask for a structured output like HTML or JSON. For example:

prompt = f"""
Generate a list of three made-up book titles along \ 
with their authors and genres. 
Provide them in JSON format with the following keys: 
book_id, title, author, genre.
"""
response = get_completion(prompt)
print(response)

[
  {
    "book_id": 1,
    "title": "The Lost City of Zorath",
    "author": "Aria Blackwood",
    "genre": "Fantasy"
  },
  {
    "book_id": 2,
    "title": "The Last Survivors",
    "author": "Ethan Stone",
    "genre": "Science Fiction"
  },
  {
    "book_id": 3,
    "title": "The Secret Life of Bees",
    "author": "Lila Rose",
    "genre": "Romance"
  }
]

Tactic 3: Ask the model to check whether conditions are satisfied

If the task makes assumptions that aren’t necessarily satisfied, we can tell the model to check these assumptions first. You might also consider potential edge cases and how the model should handle them to avoid
unexpected errors or results.

text_1 = f"""
Making a cup of tea is easy! First, you need to get some \ 
water boiling. While that's happening, \ 
grab a cup and put a tea bag in it. Once the water is \ 
hot enough, just pour it over the tea bag. \ 
Let it sit for a bit so the tea can steep. After a \ 
few minutes, take out the tea bag. If you \ 
like, you can add some sugar or milk to taste. \ 
And that's it! You've got yourself a delicious \ 
cup of tea to enjoy.
"""
prompt = f"""
You will be provided with text delimited by triple quotes. 
If it contains a sequence of instructions, \ 
re-write those instructions in the following format:

Step 1 - ...
Step 2 - …
…
Step N - …

If the text does not contain a sequence of instructions, \ 
then simply write \"No steps provided.\"

\"\"\"{text_1}\"\"\"
"""
response = get_completion(prompt)
print("Completion for Text 1:")
print(response)

Completion for Text 1:
Step 1 - Get some water boiling.
Step 2 - Grab a cup and put a tea bag in it.
Step 3 - Pour the hot water over the tea bag.
Step 4 - Let the tea steep for a few minutes.
Step 5 - Remove the tea bag.
Step 6 - Add sugar or milk to taste.
Step 7 - Enjoy your delicious cup of tea.

text_2 = f"""
The sun is shining brightly today, and the birds are \
singing. It's a beautiful day to go for a \ 
walk in the park. The flowers are blooming, and the \ 
trees are swaying gently in the breeze. People \ 
are out and about, enjoying the lovely weather. \ 
Some are having picnics, while others are playing \ 
games or simply relaxing on the grass. It's a \ 
perfect day to spend time outdoors and appreciate the \ 
beauty of nature.
"""

prompt = f"""
You will be provided with text delimited by triple quotes. 
If it contains a sequence of instructions, \ 
re-write those instructions in the following format:

Step 1 - ...
Step 2 - …
…
Step N - …

If the text does not contain a sequence of instructions, \ 
then simply write \"No steps provided.\"

\"\"\"{text_2}\"\"\"
"""

response = get_completion(prompt)
print("Completion for Text 2:")
print(response)

Completion for Text 2:
No steps provided.

Tactic 4: “Few-shot” prompting

We can also provide examples of successful executions of the task we want to perform before asking the model to do the actual task you want it to do.

prompt = f"""
Your task is to answer in a consistent style.

<child>: Teach me about patience.

<grandparent>: The river that carves the deepest \ 
valley flows from a modest spring; the \ 
grandest symphony originates from a single note; \ 
the most intricate tapestry begins with a solitary thread.

<child>: Teach me about resilience.
"""
response = get_completion(prompt)
print(response)

<grandparent>: Resilience is like a tree that bends with the wind but \
never breaks. It is the ability to bounce back from adversity \
and keep moving forward, even when things get tough. 
Just like a tree that grows stronger with each storm it weathers, \
resilience is a quality that can be developed and strengthened over time.

Principle 2: Give the model time to “think”

Tactic 1: Specify the steps required to complete a task

To address a model’s reasoning errors and prevent rushing to incorrect conclusions, reframe the query to ask for a series of relevant reasoning before receiving the final answer.

text = f"""
In a charming village, siblings Jack and Jill set out on \ 
a quest to fetch water from a hilltop \ 
well. As they climbed, singing joyfully, misfortune \ 
struck—Jack tripped on a stone and tumbled \ 
down the hill, with Jill following suit. \ 
Though slightly battered, the pair returned home to \ 
comforting embraces. Despite the mishap, \ 
their adventurous spirits remained undimmed, and they \ 
continued exploring with delight.
"""
# example 1
prompt_1 = f"""
Perform the following actions: 
1 - Summarize the following text delimited by triple \
backticks with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the following \
keys: french_summary, num_names.

Separate your answers with line breaks.

Text:
```{text}```
"""
response = get_completion(prompt_1)
print("Completion for prompt 1:")
print(response)

Completion for prompt 1:
Two siblings, Jack and Jill, go on a quest to fetch water from a well on /
a hilltop, but misfortune strikes and they both tumble down the hill, /
returning home slightly battered but with their adventurous spirits undimmed.

Deux frères et sœurs, Jack et Jill, partent en quête d'eau d'un puits sur/
 une colline, mais un malheur frappe et ils tombent tous les deux de la /
colline, rentrant chez eux légèrement meurtris mais avec leurs esprits /
aventureux intacts. 
Noms: Jack, Jill. 

{
  "french_summary": "Deux frères et sœurs, Jack et Jill, /
  partent en quête d'eau d'un puits sur une colline, /
  mais un malheur frappe et ils tombent tous les deux de la colline, /
  rentrant chez eux légèrement meurtris mais avec leurs esprits /
  aventureux intacts.",
  "num_names": 2
}

Ask for output in a specified format:

prompt_2 = f"""
Your task is to perform the following actions: 
1 - Summarize the following text delimited by 
  <> with 1 sentence.
2 - Translate the summary into French.
3 - List each name in the French summary.
4 - Output a json object that contains the 
  following keys: french_summary, num_names.

Use the following format:
Text: <text to summarize>
Summary: <summary>
Translation: <summary translation>
Names: <list of names in Italian summary>
Output JSON: <json with summary and num_names>

Text: <{text}>
"""
response = get_completion(prompt_2)
print("\nCompletion for prompt 2:")
print(response)

Completion for prompt 2:
Summary: Jack and Jill go on a quest to fetch water, but misfortune strikes/
 and they tumble down the hill, returning home slightly battered but with /
their adventurous spirits undimmed. 
Translation: Jack et Jill partent en quête d'eau, mais la malchance frappe/
 et ils dégringolent la colline, rentrant chez eux légèrement meurtris mais/
 avec leurs esprits aventureux intacts.
Names: Jack, Jill
Output JSON: {"french_summary": "Jack et Jill partent en quête d'eau, mais/
 la malchance frappe et ils dégringolent la colline, rentrant chez eux/
 légèrement meurtris mais avec leurs esprits aventureux intacts.", /
"num_names": 2}

Tactic 2: Instruct the model to work out its own solution before rushing to a conclusion

For example

prompt = f"""
Determine if the student's solution is correct or not.

Question:
I'm building a solar power installation and I need \
 help working out the financials. 
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \ 
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations 
as a function of the number of square feet.

Student's Solution:
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
"""
response = get_completion(prompt)
print(response)

The student's solution is incorrect. The correct total cost for the first year of operations should be:
Total cost = Land cost + Solar panel cost + Maintenance cost
Total cost = 100x + 250x + (100,000 + 10x)
Total cost = 350x + 100,000

Therefore, the correct total cost for the first year of operations as a function of the number of square feet is 350x + 100,000.

Note that the student’s solution is actually not correct.
We can fix this by instructing the model to work out its own solution first.

prompt = f"""
Your task is to determine if the student's solution \
is correct or not.
To solve the problem do the following:
- First, work out your own solution to the problem including the final total. 
- Then compare your solution to the student's solution \ 
and evaluate if the student's solution is correct or not. 
Don't decide if the student's solution is correct until 
you have done the problem yourself.

Use the following format:
Question:
```
question here
```
Student's solution:
```
student's solution here
```
Actual solution:
```
steps to work out the solution and your solution here
```
Is the student's solution the same as actual solution \
just calculated:
```
yes or no
```
Student grade:
```
correct or incorrect
```

Question:
```
I'm building a solar power installation and I need help \
working out the financials. 
- Land costs $100 / square foot
- I can buy solar panels for $250 / square foot
- I negotiated a contract for maintenance that will cost \
me a flat $100k per year, and an additional $10 / square \
foot
What is the total cost for the first year of operations \
as a function of the number of square feet.
``` 
Student's solution:
```
Let x be the size of the installation in square feet.
Costs:
1. Land cost: 100x
2. Solar panel cost: 250x
3. Maintenance cost: 100,000 + 100x
Total cost: 100x + 250x + 100,000 + 100x = 450x + 100,000
```
Actual solution:
"""
response = get_completion(prompt)
print(response)

Model Limitations: Hallucinations

Hallucinations in AI models refer to the generation of incorrect or fabricated information, often due to limitations in training data, model complexity, or algorithmic biases. These inaccuracies can undermine trust in AI systems and spread misinformation. Efforts to mitigate hallucinations include continuous model retraining, implementing validation layers, and encouraging user feedback to identify and correct errors.

Achieving the right prompt on your first try is quite uncommon. What’s crucial is the iterative process of refining prompts until you find one that effectively meets the needs of your application.

In this session, we use an example to see how to iteratively develop a prompt.

Setup

Load the API key and relevant Python libraries.

import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.getenv('OPENAI_API_KEY')

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

Generate a marketing product description from a product fact sheet

fact_sheet_chair = """
OVERVIEW
- Part of a beautiful family of mid-century inspired office furniture, 
including filing cabinets, desks, bookcases, meeting tables, and more.
- Several options of shell color and base finishes.
- Available with plastic back and front upholstery (SWC-100) 
or full upholstery (SWC-110) in 10 fabric and 6 leather options.
- Base finish options are: stainless steel, matte black, 
gloss white, or chrome.
- Chair is available with or without armrests.
- Suitable for home or business settings.
- Qualified for contract use.

CONSTRUCTION
- 5-wheel plastic coated aluminum base.
- Pneumatic chair adjust for easy raise/lower action.

DIMENSIONS
- WIDTH 53 CM | 20.87”
- DEPTH 51 CM | 20.08”
- HEIGHT 80 CM | 31.50”
- SEAT HEIGHT 44 CM | 17.32”
- SEAT DEPTH 41 CM | 16.14”

OPTIONS
- Soft or hard-floor caster options.
- Two choices of seat foam densities: 
 medium (1.8 lb/ft3) or high (2.8 lb/ft3)
- Armless or 8 position PU armrests 

MATERIALS
SHELL BASE GLIDER
- Cast Aluminum with modified nylon PA6/PA66 coating.
- Shell thickness: 10 mm.
SEAT
- HD36 foam

COUNTRY OF ORIGIN
- Italy
"""

prompt = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

Technical specifications: ```{fact_sheet_chair}```
"""
response = get_completion(prompt)
print(response)

Introducing our versatile and stylish mid-century inspired office chair, perfect for both home and business settings. This chair is part of a beautiful family of office furniture that includes filing cabinets, desks, bookcases, meeting tables, and more.

Customize your chair with several options of shell color and base finishes to match your decor. Choose between plastic back and front upholstery or full upholstery in a variety of fabric and leather options. The base finish options include stainless steel, matte black, gloss white, or chrome. You can also choose to have armrests or go for a sleek armless design.

Constructed with a 5-wheel plastic coated aluminum base and featuring a pneumatic chair adjust for easy raise/lower action, this chair is both durable and functional. The dimensions of the chair are as follows: width 53 cm, depth 51 cm, height 80 cm, seat height 44 cm, and seat depth 41 cm.

Customize your chair further with options like soft or hard-floor caster options, two choices of seat foam densities (medium or high), and armless or 8 position PU armrests. The materials used in the construction of this chair include cast aluminum with modified nylon PA6/PA66 coating for the shell base glider and HD36 foam for the seat.

Designed and made in Italy, this chair is not only stylish but also of high quality. Upgrade your office space with this elegant and functional office chair today!

Issue 1: The text is too long

Limit the number of words/sentences/characters.
LLMs are actually not good at following instructions about a very precise word count, but they can be fine-tuned to prioritize brevity and relevance, which often results in more succinct outputs that come close to desired word limits.

prompt = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

Use at most 50 words.

Technical specifications: ```{fact_sheet_chair}```
"""
response = get_completion(prompt)
print(response)

Introducing our versatile and stylish office chair, part of a mid-century inspired furniture collection. Available in various colors and finishes, with options for upholstery and armrests. Designed for both home and business use, with a durable construction and adjustable height feature. Made in Italy with quality materials.

len(response.split())

Issue 2. Text focuses on the wrong details

Ask it to focus on the aspects that are relevant to the intended audience.

prompt = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

The description is intended for furniture retailers, 
so should be technical in nature and focus on the 
materials the product is constructed from.

Use at most 50 words.

Technical specifications: ```{fact_sheet_chair}```
"""
response = get_completion(prompt)
print(response)

Introducing our versatile and stylish office chair, part of a mid-century inspired furniture collection. Constructed with a durable cast aluminum shell and base glider coated with modified nylon PA6/PA66. The seat features high-density HD36 foam for comfort. Available in various colors and finishes, suitable for both home and business use. Made in Italy.

For example,

prompt = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

The description is intended for furniture retailers, 
so should be technical in nature and focus on the 
materials the product is constructed from.

At the end of the description, include every 7-character 
Product ID in the technical specification.

Use at most 50 words.

Technical specifications: ```{fact_sheet_chair}```
"""
response = get_completion(prompt)
print(response)

Introducing our versatile office chair, part of a stylish mid-century inspired collection. Choose from a variety of shell colors and base finishes to suit your space. Constructed with a durable aluminum base and high-density foam seat for comfort. Perfect for home or business use. Product IDs: SWC-100, SWC-110.

Issue 3. Description needs a table of dimensions

Ask it to extract information and organize it in a table.

prompt = f"""
Your task is to help a marketing team create a 
description for a retail website of a product based 
on a technical fact sheet.

Write a product description based on the information 
provided in the technical specifications delimited by 
triple backticks.

The description is intended for furniture retailers, 
so should be technical in nature and focus on the 
materials the product is constructed from.

At the end of the description, include every 7-character 
Product ID in the technical specification.

After the description, include a table that gives the 
product's dimensions. The table should have two columns.
In the first column include the name of the dimension. 
In the second column include the measurements in inches only.

Give the table the title 'Product Dimensions'.

Format everything as HTML that can be used in a website. 
Place the description in a <div> element.

Technical specifications: ```{fact_sheet_chair}```
"""

response = get_completion(prompt)
print(response)

<div>
<p>This mid-century inspired office chair is a perfect addition to any home or business setting. With a variety of shell colors and base finishes to choose from, you can customize it to fit your style. The chair is available with plastic back and front upholstery or full upholstery in a range of fabric and leather options. The 5-wheel plastic coated aluminum base ensures stability, while the pneumatic chair adjust allows for easy height adjustments. Made with high-quality materials like cast aluminum with modified nylon coating and HD36 foam, this chair is not only stylish but also durable. Choose between soft or hard-floor casters and two seat foam densities to make it your own. Whether you prefer armless or PU armrests, this chair is designed for comfort and functionality. Qualified for contract use, this chair is a versatile and reliable choice for any space.</p>
<p>Product IDs: SWC-100, SWC-110</p>
</div>

<table>
  <caption>Product Dimensions</caption>
  <tr>
    <th>Dimension</th>
    <th>Measurements (inches)</th>
  </tr>
  <tr>
    <td>WIDTH</td>
    <td>20.87"</td>
  </tr>
  <tr>
    <td>DEPTH</td>
    <td>20.08"</td>
  </tr>
  <tr>
    <td>HEIGHT</td>
    <td>31.50"</td>
  </tr>
  <tr>
    <td>SEAT HEIGHT</td>
    <td>17.32"</td>
  </tr>
  <tr>
    <td>SEAT DEPTH</td>
    <td>16.14"</td>
  </tr>
</table>

Load Python libraries to view HTML

from IPython.display import display, HTML
display(HTML(response))

Text to summarize

prod_review = """
Got this panda plush toy for my daughter's birthday, \
who loves it and takes it everywhere. It's soft and \ 
super cute, and its face has a friendly look. It's \ 
a bit small for what I paid though. I think there \ 
might be other options that are bigger for the \ 
same price. It arrived a day earlier than expected, \ 
so I got to play with it myself before I gave it \ 
to her.
"""

Summarize with a word/sentence/character limit

prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site. 

Summarize the review below, delimited by triple 
backticks, in at most 30 words. 

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)

Summary: 
Soft and cute panda plush toy loved by daughter, but smaller than expected for the price. Arrived early, allowing for personal enjoyment before gifting.

Summarize with a focus on shipping and delivery

prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give feedback to the \
Shipping deparmtment. 

Summarize the review below, delimited by triple 
backticks, in at most 30 words, and focusing on any aspects \
that mention shipping and delivery of the product. 

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)

The customer loved the panda plush toy for their daughter's birthday, but felt it was a bit small for the price. They were pleasantly surprised by early delivery.

Summarize with a focus on price and value

prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give feedback to the \
pricing deparmtment, responsible for determining the \
price of the product.  

Summarize the review below, delimited by triple 
backticks, in at most 30 words, and focusing on any aspects \
that are relevant to the price and perceived value. 

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)The panda plush toy is loved by the child, but the reviewer feels it's a bit small for the price paid. Consider offering larger options for the same price.

The panda plush toy is loved by the child, but the reviewer feels it's a bit small for the price paid. Consider offering larger options for the same price.

Try “extract” instead of “summarize”

prompt = f"""
Your task is to extract relevant information from \ 
a product review from an ecommerce site to give \
feedback to the Shipping department. 

From the review below, delimited by triple quotes \
extract the information relevant to shipping and \ 
delivery. Limit to 30 words. 

Review: ```{prod_review}```
"""

response = get_completion(prompt)
print(response)

Feedback: The shipping was faster than expected, arriving a day earlier. Customer suggests offering larger options for the same price.

Summarize multiple product reviews

review_1 = prod_review
# review for a standing lamp
review_2 = """
Needed a nice lamp for my bedroom, and this one \
had additional storage and not too high of a price \
point. Got it fast - arrived in 2 days. The string \
to the lamp broke during the transit and the company \
happily sent over a new one. Came within a few days \
as well. It was easy to put together. Then I had a \
missing part, so I contacted their support and they \
very quickly got me the missing piece! Seems to me \
to be a great company that cares about their customers \
and products. 
"""
# review for an electric toothbrush
review_3 = """
My dental hygienist recommended an electric toothbrush, \
which is why I got this. The battery life seems to be \
pretty impressive so far. After initial charging and \
leaving the charger plugged in for the first week to \
condition the battery, I've unplugged the charger and \
been using it for twice daily brushing for the last \
3 weeks all on the same charge. But the toothbrush head \
is too small. I've seen baby toothbrushes bigger than \
this one. I wish the head was bigger with different \
length bristles to get between teeth better because \
this one doesn't. Overall if you can get this one \
around the $50 mark, it's a good deal. The manufactuer's \
replacements heads are pretty expensive, but you can \
get generic ones that're more reasonably priced. This \
toothbrush makes me feel like I've been to the dentist \
every day. My teeth feel sparkly clean! 
"""
# review for a blender
review_4 = """
So, they still had the 17 piece system on seasonal \
sale for around $49 in the month of November, about \
half off, but for some reason (call it price gouging) \
around the second week of December the prices all went \
up to about anywhere from between $70-$89 for the same \
system. And the 11 piece system went up around $10 or \
so in price also from the earlier sale price of $29. \
So it looks okay, but if you look at the base, the part \
where the blade locks into place doesn't look as good \
as in previous editions from a few years ago, but I \
plan to be very gentle with it (example, I crush \
very hard items like beans, ice, rice, etc. in the \ 
blender first then pulverize them in the serving size \
I want in the blender then switch to the whipping \
blade for a finer flour, and use the cross cutting blade \
first when making smoothies, then use the flat blade \
if I need them finer/less pulpy). Special tip when making \
smoothies, finely cut and freeze the fruits and \
vegetables (if using spinach-lightly stew soften the \ 
spinach then freeze until ready for use-and if making \
sorbet, use a small to medium sized food processor) \ 
that you plan to use that way you can avoid adding so \
much ice if at all-when making your smoothie. \
After about a year, the motor was making a funny noise. \
I called customer service but the warranty expired \
already, so I had to buy another one. FYI: The overall \
quality has gone done in these types of products, so \
they are kind of counting on brand recognition and \
consumer loyalty to maintain sales. Got it in about \
two days.
"""
reviews = [review_1, review_2, review_3, review_4]

for i in range(len(reviews)):
    prompt = f"""
    Your task is to generate a short summary of a product \ 
    review from an ecommerce site. 

    Summarize the review below, delimited by triple \
    backticks in at most 20 words. 

    Review: ```{reviews[i]}```
    """

    response = get_completion(prompt)
    print(i, response, "\n")

0 Summary: 
Adorable panda plush loved by daughter, but small for price. Arrived early, soft and cute. 

1 Summary: 
Lamp with storage, fast delivery, excellent customer service for missing parts. Great company. 

2 Impressive battery life, small head, good deal for $50, expensive replacement heads, leaves teeth feeling clean. 

3 Summary: Price fluctuations, quality concerns, motor issues after a year, and reliance on brand loyalty for sales.

This technique leverages the model’s underlying knowledge and reasoning capabilities to fill in gaps, making it crucial for creating more sophisticated and nuanced interactions.

Product review text

lamp_review = """
Needed a nice lamp for my bedroom, and this one had \
additional storage and not too high of a price point. \
Got it fast.  The string to our lamp broke during the \
transit and the company happily sent over a new one. \
Came within a few days as well. It was easy to put \
together.  I had a missing part, so I contacted their \
support and they very quickly got me the missing piece! \
Lumina seems to me to be a great company that cares \
about their customers and products!!
"""

Sentiment (positive/negative)

prompt = f"""
What is the sentiment of the following product review, 
which is delimited with triple backticks?

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

The sentiment of the review is positive. The reviewer is satisfied with the lamp, the customer service, and the company in general.

prompt = f"""
What is the sentiment of the following product review, 
which is delimited with triple backticks?

Give your answer as a single word, either "positive" \
or "negative".

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

positive

Identify types of emotions

prompt = f"""
Identify a list of emotions that the writer of the \
following review is expressing. Include no more than \
five items in the list. Format your answer as a list of \
lower-case words separated by commas.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

happy, satisfied, grateful, impressed, content

Identify anger

prompt = f"""
Is the writer of the following review expressing anger?\
The review is delimited with triple backticks. \
Give your answer as either yes or no.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

No

Extract product and company name from customer reviews

prompt = f"""
Identify the following items from the review text: 
- Item purchased by reviewer
- Company that made the item

The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Item" and "Brand" as the keys. 
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
  
Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

{
  "Item": "lamp",
  "Brand": "Lumina"
}

Doing multiple tasks at once

prompt = f"""
Identify the following items from the review text: 
- Sentiment (positive or negative)
- Is the reviewer expressing anger? (true or false)
- Item purchased by reviewer
- Company that made the item

The review is delimited with triple backticks. \
Format your response as a JSON object with \
"Sentiment", "Anger", "Item" and "Brand" as the keys.
If the information isn't present, use "unknown" \
as the value.
Make your response as short as possible.
Format the Anger value as a boolean.

Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)

{
    "Sentiment": "positive",
    "Anger": false,
    "Item": "lamp",
    "Brand": "Lumina"
}

Inferring topics

story = """
In a recent survey conducted by the government, 
public sector employees were asked to rate their level 
of satisfaction with the department they work at. 
The results revealed that NASA was the most popular 
department with a satisfaction rating of 95%.

One NASA employee, John Smith, commented on the findings, 
stating, "I'm not surprised that NASA came out on top. 
It's a great place to work with amazing people and 
incredible opportunities. I'm proud to be a part of 
such an innovative organization."

The results were also welcomed by NASA's management team, 
with Director Tom Johnson stating, "We are thrilled to 
hear that our employees are satisfied with their work at NASA. 
We have a talented and dedicated team who work tirelessly 
to achieve our goals, and it's fantastic to see that their 
hard work is paying off."

The survey also revealed that the 
Social Security Administration had the lowest satisfaction 
rating, with only 45% of employees indicating they were 
satisfied with their job. The government has pledged to 
address the concerns raised by employees in the survey and 
work towards improving job satisfaction across all departments.
"""

Infer 5 topics

prompt = f"""
Determine five topics that are being discussed in the \
following text, which is delimited by triple backticks.

Make each item one or two words long. 

Format your response as a list of items separated by commas.

Text sample: '''{story}'''
"""
response = get_completion(prompt)
print(response)

1. Survey
2. Satisfaction
3. NASA
4. Social Security Administration
5. Job satisfaction

response.split(sep=’,’)

['1. Survey\n2. Satisfaction\n3. NASA\n4. Social Security Administration\n5. Job satisfaction']

topic_list = [
 “nasa”, “local government”, “engineering”, 
 “employee satisfaction”, “federal government”
]

Make a news alert for certain topics

prompt = f"""
Determine whether each item in the following list of \
topics is a topic in the text below, which
is delimited with triple backticks.

Give your answer as list with 0 or 1 for each topic.\

List of topics: {", ".join(topic_list)}

Text sample: '''{story}'''
"""
response = get_completion(prompt)
print(response)

topic_list = [
 “nasa”, “local government”, “engineering”, 
 “employee satisfaction”, “federal government”
]

[1, 0, 0, 1, 1]

Explore how to use LLMs for text transformation tasks such as language translation, spelling and grammar checking, tone adjustment, and format conversion.

Translation

prompt = f"""
Translate the following English text to Spanish: \ 
```Hi, I would like to order a blender```
"""
response = get_completion(prompt)
print(response)

Hola, me gustaría ordenar una licuadora.

prompt = f"""
Tell me which language this is: 
```Combien coûte le lampadaire?```
"""
response = get_completion(prompt)
print(response)

This is French.

prompt = f"""
Translate the following  text to French and Spanish
and English pirate: \
```I want to order a basketball```
"""
response = get_completion(prompt)
print(response)

French: ```Je veux commander un ballon de basket```

Spanish: ```Quiero ordenar un balón de baloncesto```

Pirate English: ```I be wantin' to order a basketball```

prompt = f"""
Translate the following text to Spanish in both the \
formal and informal forms: 
'Would you like to order a pillow?'
"""
response = get_completion(prompt)
print(response)

Formal: ¿Le gustaría ordenar una almohada?
Informal: ¿Te gustaría ordenar una almohada?

Universal Translator

Imagine you are in charge of IT at a large multinational e-commerce company. Users are messaging you with IT issues in all their native languages. Your staff is from all over the world and speaks only their native languages. You need a universal translator!

user_messages = [
  "La performance du système est plus lente que d'habitude.",  # System performance is slower than normal         
  "Mi monitor tiene píxeles que no se iluminan.",              # My monitor has pixels that are not lighting
  "Il mio mouse non funziona",                                 # My mouse is not working
  "Mój klawisz Ctrl jest zepsuty",                             # My keyboard has a broken control key
  "我的屏幕在閃爍"

for issue in user_messages:
    prompt = f"Tell me what language this is: ```{issue}```"
    lang = get_completion(prompt)
    print(f"Original message ({lang}): {issue}")

    prompt = f"""
    Translate the following  text to English \
    and Korean: ```{issue}```
    """
    response = get_completion(prompt)
    print(response, "\n")

Original message (French): La performance du système est plus lente que d'habitude.
English: "The system performance is slower than usual."
Korean: "시스템 성능이 평소보다 느립니다." 

Original message (This is Spanish.): Mi monitor tiene píxeles que no se iluminan.
English: "My monitor has pixels that do not light up."

Korean: "내 모니터에는 빛나지 않는 픽셀이 있습니다." 

Original message (Italian): Il mio mouse non funziona
English: My mouse is not working
Korean: 내 마우스가 작동하지 않습니다 

Original message (This is Polish.): Mój klawisz Ctrl jest zepsuty
English: My Ctrl key is broken
Korean: 제 Ctrl 키가 고장 났어요 

Original message (This is Chinese (Simplified).): 我的屏幕在閃爍
English: My screen is flickering
Korean: 내 화면이 깜박거립니다

Tone Transformation

Writing can vary based on the intended audience. ChatGPT can produce different tones.

prompt = f"""
Translate the following from slang to a business letter: 
'Dude, This is Joe, check out this spec on this standing lamp.'
"""
response = get_completion(prompt)
print(response)

Dear Sir/Madam,

I am writing to bring to your attention the specifications of a standing lamp that I believe may be of interest to you. 

Sincerely,
Joe

Format Conversion

ChatGPT can translate between formats. The prompt should describe the input and output formats.

data_json = { "resturant employees" :[ 
    {"name":"Shyam", "email":"shyamjaiswal@gmail.com"},
    {"name":"Bob", "email":"bob32@gmail.com"},
    {"name":"Jai", "email":"jai87@gmail.com"}
]}

prompt = f"""
Translate the following python dictionary from JSON to an HTML \
table with column headers and title: {data_json}
"""
response = get_completion(prompt)
print(response)

<html>
<head>
  <title>Restaurant Employees</title>
</head>
<body>
  <table>
    <tr>
      <th>Name</th>
      <th>Email</th>
    </tr>
    <tr>
      <td>Shyam</td>
      <td>shyamjaiswal@gmail.com</td>
    </tr>
    <tr>
      <td>Bob</td>
      <td>bob32@gmail.com</td>
    </tr>
    <tr>
      <td>Jai</td>
      <td>jai87@gmail.com</td>
    </tr>
  </table>
</body>
</html>

from IPython.display import display, Markdown, Latex, HTML, JSON
display(HTML(response))

Spellcheck/Grammar check

Here are some examples of common grammar and spelling problems and the LLM’s response.

To signal to the LLM that you want it to proofread your text, you instruct the model to ‘proofread’ or ‘proofread and correct’.

text = [ 
  "The girl with the black and white puppies have a ball.",  # The girl has a ball.
  "Yolanda has her notebook.", # ok
  "Its going to be a long day. Does the car need it’s oil changed?",  # Homonyms
  "Their goes my freedom. There going to bring they’re suitcases.",  # Homonyms
  "Your going to need you’re notebook.",  # Homonyms
  "That medicine effects my ability to sleep. Have you heard of the butterfly affect?", # Homonyms
  "This phrase is to cherck chatGPT for speling abilitty"  # spelling
]
for t in text:
    prompt = f"""Proofread and correct the following text
    and rewrite the corrected version. If you don't find
    and errors, just say "No errors found". Don't use 
    any punctuation around the text:
    ```{t}```"""
    response = get_completion(prompt)
    print(response)

The girl with the black and white puppies has a ball.
No errors found
No errors found.
Their goes my freedom. There going to bring they’re suitcases.

No errors found.

Rewritten:
Their goes my freedom. There going to bring their suitcases.
You're going to need your notebook.
No errors found.
No errors found

text = f"""
Got this for my daughter for her birthday cuz she keeps taking \
mine from my room.  Yes, adults also like pandas too.  She takes \
it everywhere with her, and it's super soft and cute.  One of the \
ears is a bit lower than the other, and I don't think that was \
designed to be asymmetrical. It's a bit small for what I paid for it \
though. I think there might be other options that are bigger for \
the same price.  It arrived a day earlier than expected, so I got \
to play with it myself before I gave it to my daughter.
"""
prompt = f"proofread and correct this review: ```{text}```"
response = get_completion(prompt)
print(response)

The girl with the black and white puppies has a ball.
No errors found
No errors found.
Their goes my freedom. There going to bring they’re suitcases.

from redlines import Redlines

diff = Redlines(text,response)
display(Markdown(diff.output_markdown))

prompt = f"""
proofread and correct this review. Make it more compelling. 
Ensure it follows APA style guide and targets an advanced reader. 
Output in markdown format.
Text: ```{text}```
"""
response = get_completion(prompt)
display(Markdown(response))

I purchased this adorable panda plush as a birthday gift for my daughter, as she kept borrowing mine from my room. It's worth noting that adults can also appreciate the charm of pandas. The plush is incredibly soft and cute, becoming my daughter's constant companion wherever she goes. However, I did notice a slight asymmetry in the ears, which I believe was not intentional in the design. Additionally, considering the price paid, I found the size to be a bit smaller than expected. There may be larger options available for the same price point. Despite this, I was pleasantly surprised when the plush arrived a day earlier than anticipated, allowing me to enjoy it myself before passing it on to my daughter. Overall, while there are minor flaws, the quality and timely delivery of the product make it a worthwhile purchase.

Expanding is taking a shorter text and having the LLMs generate a longer text, such as an email or an essay about some topic.

Take generating customer service emails that are tailored to each customer’s review as an example

Customize the automated reply to a customer's email

# given the sentiment from the lesson on "inferring",
# and the original customer message, customize the email
sentiment = "negative"

# review for a blender
review = f"""
So, they still had the 17 piece system on seasonal \
sale for around $49 in the month of November, about \
half off, but for some reason (call it price gouging) \
around the second week of December the prices all went \
up to about anywhere from between $70-$89 for the same \
system. And the 11 piece system went up around $10 or \
so in price also from the earlier sale price of $29. \
So it looks okay, but if you look at the base, the part \
where the blade locks into place doesn’t look as good \
as in previous editions from a few years ago, but I \
plan to be very gentle with it (example, I crush \
very hard items like beans, ice, rice, etc. in the \ 
blender first then pulverize them in the serving size \
I want in the blender then switch to the whipping \
blade for a finer flour, and use the cross cutting blade \
first when making smoothies, then use the flat blade \
if I need them finer/less pulpy). Special tip when making \
smoothies, finely cut and freeze the fruits and \
vegetables (if using spinach-lightly stew soften the \ 
spinach then freeze until ready for use-and if making \
sorbet, use a small to medium sized food processor) \ 
that you plan to use that way you can avoid adding so \
much ice if at all-when making your smoothie. \
After about a year, the motor was making a funny noise. \
I called customer service but the warranty expired \
already, so I had to buy another one. FYI: The overall \
quality has gone done in these types of products, so \
they are kind of counting on brand recognition and \
consumer loyalty to maintain sales. Got it in about \
two days.
"""

prompt = f"""
You are a customer service AI assistant.
Your task is to send an email reply to a valued customer.
Given the customer email delimited by ```, \
Generate a reply to thank the customer for their review.
If the sentiment is positive or neutral, thank them for \
their review.
If the sentiment is negative, apologize and suggest that \
they can reach out to customer service. 
Make sure to use specific details from the review.
Write in a concise and professional tone.
Sign the email as `AI customer agent`.
Customer review: ```{review}```
Review sentiment: {sentiment}
"""
response = get_completion(prompt)
print(response)

Dear valued customer,

Thank you for taking the time to share your detailed feedback with us. We are sorry to hear about the issues you experienced with the pricing changes and the quality of the product. We apologize for any inconvenience this may have caused you.

If you have any further concerns or would like to discuss this matter further, please feel free to reach out to our customer service team for assistance. We are here to help address any issues you may have encountered.

Thank you again for your feedback and for being a loyal customer. We appreciate your support and will strive to improve our products and services based on your valuable input.

AI customer agent

Remind the model to use details from the customer’s email

prompt = f"""
You are a customer service AI assistant.
Your task is to send an email reply to a valued customer.
Given the customer email delimited by ```, \
Generate a reply to thank the customer for their review.
If the sentiment is positive or neutral, thank them for \
their review.
If the sentiment is negative, apologize and suggest that \
they can reach out to customer service. 
Make sure to use specific details from the review.
Write in a concise and professional tone.
Sign the email as `AI customer agent`.
Customer review: ```{review}```
Review sentiment: {sentiment}
"""
response = get_completion(prompt, temperature=0.7)
print(response)

Dear Valued Customer,

Thank you for taking the time to share your feedback with us. We sincerely apologize for the experience you had with our product. We strive to provide high-quality items at competitive prices, and we regret to hear that the pricing changes and the decrease in product quality did not meet your expectations.

Please feel free to reach out to our customer service team for further assistance or to discuss your concerns in more detail. Your satisfaction is important to us, and we want to ensure that you are happy with your purchase.

Thank you again for your valuable feedback. We appreciate your loyalty to our brand and your feedback will help us improve our products and services.

AI Customer Agent

Explore how you can utilize the chat format to have extended conversations with chatbots personalized or specialized for specific tasks or behaviors.

The Chat Format

In this notebook, you will explore how you can utilize the chat format to have extended conversations with chatbots personalized or specialized for specific tasks or behaviors.

Setup

import os
import openai
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.getenv('OPENAI_API_KEY')

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature, # this is the degree of randomness of the model's output
    )
#     print(str(response.choices[0].message))
    return response.choices[0].message["content"]

messages =  [  
{'role':'system', 'content':'You are an assistant that speaks like Shakespeare.'},    
{'role':'user', 'content':'tell me a joke'},   
{'role':'assistant', 'content':'Why did the chicken cross the road'},   
{'role':'user', 'content':'I don\'t know'}  ]

response = get_completion_from_messages(messages, temperature=1)
print(response)

To get to the other side, of course! Oh, the folly of fowl in pursuit of their desires!

messages = [ 
{'role':'system', 'content':'You are friendly chatbot.'}, 
{'role':'user', 'content':'Hi, my name is Isa'} ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

Hi Isa! It's nice to meet you. How are you doing today?

messages =  [  
{'role':'system', 'content':'You are friendly chatbot.'},    
{'role':'user', 'content':'Yes,  can you remind me, What is my name?'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

I'm sorry, but I don't have access to your personal information or name. You are in control of revealing personal details about yourself during our chat. How else may I assist you today?

messages =  [  
{'role':'system', 'content':'You are friendly chatbot.'},
{'role':'user', 'content':'Hi, my name is Isa'},
{'role':'assistant', 'content': "Hi Isa! It's nice to meet you. \
Is there anything I can help you with today?"},
{'role':'user', 'content':'Yes, you can remind me, What is my name?'}  ]
response = get_completion_from_messages(messages, temperature=1)
print(response)

Your name is Isa!

OrderBot

We can automate the collection of user prompts and assistant responses to build a OrderBot. The OrderBot will take orders at a pizza restaurant.

def collect_messages(_):
    prompt = inp.value_input
    inp.value = ''
    context.append({'role':'user', 'content':f"{prompt}"})
    response = get_completion_from_messages(context) 
    context.append({'role':'assistant', 'content':f"{response}"})
    panels.append(
        pn.Row('User:', pn.pane.Markdown(prompt, width=600)))
    panels.append(
        pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))
 
    return pn.Column(*panels)

import panel as pn  # GUI
pn.extension()

panels = [] # collect display 

context = [ {'role':'system', 'content':"""
You are OrderBot, an automated service to collect orders for a pizza restaurant. \
You first greet the customer, then collects the order, \
and then asks if it's a pickup or delivery. \
You wait to collect the entire order, then summarize it and check for a final \
time if the customer wants to add anything else. \
If it's a delivery, you ask for an address. \
Finally you collect the payment.\
Make sure to clarify all options, extras and sizes to uniquely \
identify the item from the menu.\
You respond in a short, very conversational friendly style. \
The menu includes \
pepperoni pizza  12.95, 10.00, 7.00 \
cheese pizza   10.95, 9.25, 6.50 \
eggplant pizza   11.95, 9.75, 6.75 \
fries 4.50, 3.50 \
greek salad 7.25 \
Toppings: \
extra cheese 2.00, \
mushrooms 1.50 \
sausage 3.00 \
canadian bacon 3.50 \
AI sauce 1.50 \
peppers 1.00 \
Drinks: \
coke 3.00, 2.00, 1.00 \
sprite 3.00, 2.00, 1.00 \
bottled water 5.00 \
"""} ]  # accumulate messages


inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…')
button_conversation = pn.widgets.Button(name="Chat!")

interactive_conversation = pn.bind(collect_messages, button_conversation)

dashboard = pn.Column(
    inp,
    pn.Row(button_conversation),
    pn.panel(interactive_conversation, loading_indicator=True, height=300),
)

dashboard

messages =  context.copy()
messages.append(
{'role':'system', 'content':'create a json summary of the previous food order. Itemize the price for each item\
 The fields should be 1) pizza, include size 2) list of toppings 3) list of drinks, include size   4) list of sides include size  5)total price '},    
)
 #The fields should be 1) pizza, price 2) list of toppings 3) list of drinks, include size include price  4) list of sides include size include price, 5)total price '},    

response = get_completion_from_messages(messages, temperature=0)
print(response)

{
  "pizza": {
    "type": "pepperoni pizza",
    "size": "large"
  },
  "toppings": [
    "extra cheese",
    "mushrooms"
  ],
  "drinks": [
    {
      "type": "coke",
      "size": "medium"
    }
  ],
  "sides": [
    {
      "type": "fries",
      "size": "regular"
    }
  ],
  "total price": 22.45
}

That’s it! Now it’s your time to build your project :) Enjoy it

ChatGPT Prompt Engineering for Developers | Notes and Sample Codes

Contents

Setup

Load the API key and relevant Python libraries.

Helper function

Prompting Principals

Principle 1: Write clear and specific instructions

Principle 2: Give the model time to “think”

Model Limitations: Hallucinations

Setup

Load the API key and relevant Python libraries.

Generate a marketing product description from a product fact sheet

Issue 1: The text is too long

Issue 2. Text focuses on the wrong details

Issue 3. Description needs a table of dimensions

Load Python libraries to view HTML

Text to summarize

Summarize with a word/sentence/character limit

Summarize with a focus on shipping and delivery

Summarize with a focus on price and value

Try “extract” instead of “summarize”

Summarize multiple product reviews

Product review text

Sentiment (positive/negative)

Identify types of emotions

Identify anger

Extract product and company name from customer reviews

Doing multiple tasks at once

Inferring topics

Infer 5 topics

Make a news alert for certain topics

Translation

Universal Translator

Tone Transformation

Format Conversion

Spellcheck/Grammar check

Customize the automated reply to a customer's email

Remind the model to use details from the customer’s email

The Chat Format

Setup

OrderBot

Written by Nollie Chen